景观和距离对基于音频的鸟类自动识别物种的影响
Panu Somervuo1, Patrik Lauha1, Tapio Lokki2
1Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
The Journal of the Acoustical Society of America
|July 13, 2023
概括
景观和距离显著影响自动鸟类物种识别. 考虑到这些环境因素,可提高声学监测中的识别准确性.
科学领域:
- 生物声学是一种生物声学.
- 生态生态学 生态生态学
- 机器学习 机器学习
背景情况:
- 自动物种识别依赖于准确分类动物的声音.
- 景观和距离等环境因素可能会扭曲声音记录,影响识别准确度.
- 现有的方法往往不能完全解释这些声学传播效应.
研究的目的:
- 调查景观和距离对自动物种识别准确性的影响.
- 开发和验证一种结合景观和距离效应的方法,以改进声学识别.
- 为了证明提出的方法的普遍适用性,超出了鸟类的声音.
主要方法:
- 测量声源和录音单元之间的冲动响应,以描述景观效应.
- 将测量的冲动响应与现有的声音记录相结合,以模拟环境扭曲.
- 使用自主记录单元在不同的环境 (开放的田野,森林) 测试方法,在不同的距离.
- 建筑分类模型有或没有考虑到景观和距离的影响.
主要成果:
- 当考虑景观和距离的影响时,物种识别准确性显著提高.
- 提出的方法有效地模拟了不同环境对声音传播的影响.
- 在各种距离和息地类型中证明了它的有效性.
结论:
- 将景观和距离效应纳入声学识别模型对于提高准确性至关重要.
- 脉冲响应卷积方法提供了一种强大的方法来增强生物声学监测.
- 这种技术适用于识别各种动物和非动物声音.
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